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    context manager::

        >>> with raises(ZeroDivisionError):
        ...    1/0

    .. versionchanged:: 2.10

    In the context manager form you may use the keyword argument
    ``message`` to specify a custom failure message::

        >>> with raises(ZeroDivisionError, message="Expecting ZeroDivisionError"):
        ...    pass
        Traceback (most recent call last):
          ...
        Failed: Expecting ZeroDivisionError


    .. note::

       When using ``pytest.raises`` as a context manager, it's worthwhile to
       note that normal context manager rules apply and that the exception
       raised *must* be the final line in the scope of the context manager.
       Lines of code after that, within the scope of the context manager will
       not be executed. For example::

           >>> value = 15
           >>> with raises(ValueError) as exc_info:
           ...     if value > 10:
           ...         raise ValueError("value must be <= 10")
           ...     assert exc_info.type == ValueError  # this will not execute

       Instead, the following approach must be taken (note the difference in
       scope)::

           >>> with raises(ValueError) as exc_info:
           ...     if value > 10:
           ...         raise ValueError("value must be <= 10")
           ...
           >>> assert exc_info.type == ValueError

    Or you can use the keyword argument ``match`` to assert that the
    exception matches a text or regex::

        >>> with raises(ValueError, match='must be 0 or None'):
        ...     raise ValueError("value must be 0 or None")

        >>> with raises(ValueError, match=r'must be \d+$'):
        ...     raise ValueError("value must be 42")


    Or you can specify a callable by passing a to-be-called lambda::

        >>> raises(ZeroDivisionError, lambda: 1/0)
        <ExceptionInfo ...>

    or you can specify an arbitrary callable with arguments::

        >>> def f(x): return 1/x
        ...
        >>> raises(ZeroDivisionError, f, 0)
        <ExceptionInfo ...>
        >>> raises(ZeroDivisionError, f, x=0)
        <ExceptionInfo ...>

    A third possibility is to use a string to be executed::

        >>> raises(ZeroDivisionError, "f(0)")
        <ExceptionInfo ...>

    .. autoclass:: _pytest._code.ExceptionInfo
        :members:

    .. note::
        Similar to caught exception objects in Python, explicitly clearing
        local references to returned ``ExceptionInfo`` objects can
        help the Python interpreter speed up its garbage collection.

        Clearing those references breaks a reference cycle
        (``ExceptionInfo`` --> caught exception --> frame stack raising
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        ``ExceptionInfo``) which makes Python keep all objects referenced
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    within some tolerance.

    Due to the `intricacies of floating-point arithmetic`__, numbers that we
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        >>> 0.1 + 0.2 == 0.3
        False

    __ https://docs.python.org/3/tutorial/floatingpoint.html

    This problem is commonly encountered when writing tests, e.g. when making
    sure that floating-point values are what you expect them to be.  One way to
    deal with this problem is to assert that two floating-point numbers are
    equal to within some appropriate tolerance::

        >>> abs((0.1 + 0.2) - 0.3) < 1e-6
        True

    However, comparisons like this are tedious to write and difficult to
    understand.  Furthermore, absolute comparisons like the one above are
    usually discouraged because there's no tolerance that works well for all
    situations.  ``1e-6`` is good for numbers around ``1``, but too small for
    very big numbers and too big for very small ones.  It's better to express
    the tolerance as a fraction of the expected value, but relative comparisons
    like that are even more difficult to write correctly and concisely.

    The ``approx`` class performs floating-point comparisons using a syntax
    that's as intuitive as possible::

        >>> from pytest import approx
        >>> 0.1 + 0.2 == approx(0.3)
        True

    The same syntax also works on sequences of numbers::

        >>> (0.1 + 0.2, 0.2 + 0.4) == approx((0.3, 0.6))
        True

    By default, ``approx`` considers numbers within a relative tolerance of
    ``1e-6`` (i.e. one part in a million) of its expected value to be equal.
    This treatment would lead to surprising results if the expected value was
    ``0.0``, because nothing but ``0.0`` itself is relatively close to ``0.0``.
    To handle this case less surprisingly, ``approx`` also considers numbers
    within an absolute tolerance of ``1e-12`` of its expected value to be
    equal.  Infinite numbers are another special case.  They are only
    considered equal to themselves, regardless of the relative tolerance.  Both
    the relative and absolute tolerances can be changed by passing arguments to
    the ``approx`` constructor::

        >>> 1.0001 == approx(1)
        False
        >>> 1.0001 == approx(1, rel=1e-3)
        True
        >>> 1.0001 == approx(1, abs=1e-3)
        True

    If you specify ``abs`` but not ``rel``, the comparison will not consider
    the relative tolerance at all.  In other words, two numbers that are within
    the default relative tolerance of ``1e-6`` will still be considered unequal
    if they exceed the specified absolute tolerance.  If you specify both
    ``abs`` and ``rel``, the numbers will be considered equal if either
    tolerance is met::

        >>> 1 + 1e-8 == approx(1)
        True
        >>> 1 + 1e-8 == approx(1, abs=1e-12)
        False
        >>> 1 + 1e-8 == approx(1, rel=1e-6, abs=1e-12)
        True

    If you're thinking about using ``approx``, then you might want to know how
    it compares to other good ways of comparing floating-point numbers.  All of
    these algorithms are based on relative and absolute tolerances and should
    agree for the most part, but they do have meaningful differences:

    - ``math.isclose(a, b, rel_tol=1e-9, abs_tol=0.0)``:  True if the relative
      tolerance is met w.r.t. either ``a`` or ``b`` or if the absolute
      tolerance is met.  Because the relative tolerance is calculated w.r.t.
      both ``a`` and ``b``, this test is symmetric (i.e.  neither ``a`` nor
      ``b`` is a "reference value").  You have to specify an absolute tolerance
      if you want to compare to ``0.0`` because there is no tolerance by
      default.  Only available in python>=3.5.  `More information...`__

      __ https://docs.python.org/3/library/math.html#math.isclose

    - ``numpy.isclose(a, b, rtol=1e-5, atol=1e-8)``: True if the difference
      between ``a`` and ``b`` is less that the sum of the relative tolerance
      w.r.t. ``b`` and the absolute tolerance.  Because the relative tolerance
      is only calculated w.r.t. ``b``, this test is asymmetric and you can
      think of ``b`` as the reference value.  Support for comparing sequences
      is provided by ``numpy.allclose``.  `More information...`__

      __ http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.isclose.html

    - ``unittest.TestCase.assertAlmostEqual(a, b)``: True if ``a`` and ``b``
      are within an absolute tolerance of ``1e-7``.  No relative tolerance is
      considered and the absolute tolerance cannot be changed, so this function
      is not appropriate for very large or very small numbers.  Also, it's only
      available in subclasses of ``unittest.TestCase`` and it's ugly because it
      doesn't follow PEP8.  `More information...`__

      __ https://docs.python.org/3/library/unittest.html#unittest.TestCase.assertAlmostEqual

    - ``a == pytest.approx(b, rel=1e-6, abs=1e-12)``: True if the relative
      tolerance is met w.r.t. ``b`` or if the absolute tolerance is met.
      Because the relative tolerance is only calculated w.r.t. ``b``, this test
      is asymmetric and you can think of ``b`` as the reference value.  In the
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